Explainable Machine Learning Professors in Global

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David Inouye
Purdue University United States of America
explainability
trustworthy machine learning
causal machine learning
robust machine learning
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Mateja Jamnik
University of Cambridge United Kingdom
explainability
automated reasoning
diagrammatic reasoning
machine learning
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Henrik Boström
KTH Royal Institute of Technology Sweden
explainable machine learning
trustworthy machine learning
probabilistic machine learning
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Yingzhen Li
Imperial College London United Kingdom
explainable machine learning
probabilistic machine learning
machine learning
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Klaus-robert Müller
Technical University of Berlin Germany
explainable ai
probabilistic machine learning
machine learning
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Klaus Müller
Technical University of Berlin Germany
explainable ai
probabilistic machine learning
machine learning
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Michael Guerzhoy
University of Toronto Canada
interpretable machine learning
mechanistic interpretability
machine learning
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T. Rienstra
Maastricht University Netherlands
explainable ai
reasoning
computational argumentation
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Hana Chockler
King's College London United Kingdom
explainable ai
machine learning
causal reasoning
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Fei Wang
Cornell University Netherlands
explainability
model interpretability
machine learning
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Fei Wang
Cornell University Netherlands
explainability
model interpretability
machine learning
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Matthias Wilms
University of Michigan United States of America
explainability
interpretability
machine learning
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Holger Hoos
RWTH Aachen University Germany
automated reasoning
automated machine learning
machine learning
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Moez Ali
Queen's University Canada
model interpretability
automated machine learning
machine learning
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Thomas Icard
Stanford University United States of America
automated reasoning
probabilistic reasoning
machine learning
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David Poole
University of British Columbia Canada
automated reasoning
probabilistic reasoning
machine learning
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Tim French
University of Western Australia Australia
automated reasoning
probabilistic reasoning
reasoning
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Navid Azizan
Massachusetts Institute of Technology United States of America
trustworthy machine learning
scientific machine learning
machine learning
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Mateja Jamnik
University of Cambridge United Kingdom
automated reasoning
diagrammatic reasoning
machine learning
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Percy Liang
Stanford University United States of America
interpretability
machine learning
reasoning
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Geoff Webb
Monash University Australia
scalable learning
machine learning
interactive machine learning
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